


import pickle

import numpy as np
from matplotlib import pyplot as plt
class 		__magic_name__						:





     '''simple docstring'''



     def __init__(					self:List[str]     ,		_a:str     ,		_a:Optional[Any]     ,		_a:Union[str, Any]     ,		_a:Union[str, Any]     ,		_a:Optional[Any]     ,		_a:int=0.2     ,		_a:Optional[int]=0.2   ):
       snake_case__						= bp_numa
       snake_case__						= bp_numa
       snake_case__						= bp_numa
       snake_case__						= conva_get[:2]
       snake_case__						= conva_get[2]
       snake_case__						= size_pa
       snake_case__						= rate_w
       snake_case__						= rate_t
       snake_case__						= [
           np.mat(-1 * np.random.rand(self.conva[0]     ,		self.conva[0]   ) + 0.5   )
           for i in range(self.conva[1]   )
       ]
       snake_case__						= np.mat(-1 * np.random.rand(self.num_bpa     ,		self.num_bpa   ) + 0.5   )
       snake_case__						= np.mat(-1 * np.random.rand(self.num_bpa     ,		self.num_bpa   ) + 0.5   )
       snake_case__						= -2 * np.random.rand(self.conva[1]   ) + 1
       snake_case__						= -2 * np.random.rand(self.num_bpa   ) + 1
       snake_case__						= -2 * np.random.rand(self.num_bpa   ) + 1

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:Optional[Any]   ):
       # save model dict with pickle
       snake_case__						= {
           '''num_bp1''': self.num_bpa,
           '''num_bp2''': self.num_bpa,
           '''num_bp3''': self.num_bpa,
           '''conv1''': self.conva,
           '''step_conv1''': self.step_conva,
           '''size_pooling1''': self.size_poolinga,
           '''rate_weight''': self.rate_weight,
           '''rate_thre''': self.rate_thre,
           '''w_conv1''': self.w_conva,
           '''wkj''': self.wkj,
           '''vji''': self.vji,
           '''thre_conv1''': self.thre_conva,
           '''thre_bp2''': self.thre_bpa,
           '''thre_bp3''': self.thre_bpa,
       }
       with open(_a     ,		'''wb'''   ) as f:
         pickle.dump(_a     ,		_a   )

       print(F"""Model saved： {save_path}"""   )

     @classmethod
     def 	SCREAMING_SNAKE_CASE__						(					cls:Any     ,		_a:Tuple   ):
       # read saved model
       with open(_a     ,		'''rb'''   ) as f:
         snake_case__						= pickle.load(_a   )  # noqa: S301

       snake_case__						= model_dic.get('''conv1'''   )
       conv_get.append(model_dic.get('''step_conv1'''   )   )
       snake_case__						= model_dic.get('''size_pooling1'''   )
       snake_case__						= model_dic.get('''num_bp1'''   )
       snake_case__						= model_dic.get('''num_bp2'''   )
       snake_case__						= model_dic.get('''num_bp3'''   )
       snake_case__						= model_dic.get('''rate_weight'''   )
       snake_case__						= model_dic.get('''rate_thre'''   )
       # create model instance
       snake_case__						= CNN(_a     ,		_a     ,		_a     ,		_a     ,		_a     ,		_a     ,		_a   )
       # modify model parameter
       snake_case__						= model_dic.get('''w_conv1'''   )
       snake_case__						= model_dic.get('''wkj'''   )
       snake_case__						= model_dic.get('''vji'''   )
       snake_case__						= model_dic.get('''thre_conv1'''   )
       snake_case__						= model_dic.get('''thre_bp2'''   )
       snake_case__						= model_dic.get('''thre_bp3'''   )
       return conv_ins

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:int   ):
       return 1 / (1 + np.exp(-1 * x   ))

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:Dict   ):
       return round(_a     ,		3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Any     ,		_a:Optional[Any]     ,		_a:Union[str, Any]     ,		_a:int     ,		_a:List[Any]   ):
       # convolution process
       snake_case__						= convs[0]
       snake_case__						= convs[1]
       snake_case__						= np.shape(_a   )[0]
       # get the data slice of original image data, data_focus
       snake_case__						= []
       for i_focus in range(0     ,		size_data - size_conv + 1     ,		_a   ):
         for j_focus in range(0     ,		size_data - size_conv + 1     ,		_a   ):
           snake_case__						= data[
               i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
           ]
           data_focus.append(_a   )
        # calculate the feature map of every single kernel, and saved as list of matrix
       snake_case__						= []
       snake_case__						= int((size_data - size_conv) / conv_step + 1   )
       for i_map in range(_a   ):
         snake_case__						= []
         for i_focus in range(len(_a   )   ):
           snake_case__						= (
               np.sum(np.multiply(data_focus[i_focus]     ,		w_convs[i_map]   )   )
               - thre_convs[i_map]
           )
           featuremap.append(self.sig(_a   )   )
         snake_case__						= np.asmatrix(_a   ).reshape(
             _a     ,		_a   )
         data_featuremap.append(_a   )

       # expanding the data slice to One dimenssion
       snake_case__						= []
       for each_focus in data_focus:
         focusa_list.extend(self.Expand_Mat(_a   )   )
       snake_case__						= np.asarray(_a   )
       return focus_list, data_featuremap

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:List[str]     ,		_a:str     ,		_a:List[Any]="average_pool"   ):
       # pooling process
       snake_case__						= len(featuremaps[0]   )
       snake_case__						= int(size_map / size_pooling   )
       snake_case__						= []
       for i_map in range(len(_a   )   ):
         snake_case__						= featuremaps[i_map]
         snake_case__						= []
         for i_focus in range(0     ,		_a     ,		_a   ):
           for j_focus in range(0     ,		_a     ,		_a   ):
             snake_case__						= feature_map[
                 i_focus : i_focus + size_pooling,
                 j_focus : j_focus + size_pooling,
             ]
             if pooling_type == "average_pool":
               # average pooling
               map_pooled.append(np.average(_a   )   )
             elif pooling_type == "max_pooling":
               # max pooling
               map_pooled.append(np.max(_a   )   )
         snake_case__						= np.asmatrix(_a   ).reshape(_a     ,		_a   )
         featuremap_pooled.append(_a   )
       return featuremap_pooled

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Union[str, Any]   ):
       # expanding three dimension data to one dimension list
       snake_case__						= []
       for i in range(len(_a   )   ):
         snake_case__						= np.shape(data[i]   )
         snake_case__						= data[i].reshape(1     ,		shapes[0] * shapes[1]   )
         snake_case__						= data_listed.getA().tolist()[0]
         data_expanded.extend(_a   )
       snake_case__						= np.asarray(_a   )
       return data_expanded

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:Optional[Any]   ):
       # expanding matrix to one dimension list
       snake_case__						= np.asarray(_a   )
       snake_case__						= np.shape(_a   )
       snake_case__						= data_mat.reshape(1     ,		shapes[0] * shapes[1]   )
       return data_expanded

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:List[Any]     ,		_a:Any     ,		_a:str     ,		_a:Tuple     ,		_a:Union[str, Any]   ):
       snake_case__						= []
       snake_case__						= 0
       for i_map in range(_a   ):
         snake_case__						= np.ones((size_map, size_map)   )
         for i in range(0     ,		_a     ,		_a   ):
           for j in range(0     ,		_a     ,		_a   ):
             snake_case__						= pd_pool[
                 i_pool
             ]
             snake_case__						= i_pool + 1
         snake_case__						= np.multiply(
             _a     ,		np.multiply(out_map[i_map]     ,		(1 - out_map[i_map])   )   )
         pd_all.append(_a   )
       return pd_all

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:Dict     ,		_a:List[str]     ,		_a:Optional[int]     ,		_a:Tuple     ,		_a:Any     ,		_a:Dict=bool   ):
       # model traning
       print('''----------------------Start Training-------------------------'''   )
       print((''' - - Shape: Train_Data  ''', np.shape(_a   ))   )
       print((''' - - Shape: Teach_Data  ''', np.shape(_a   ))   )
       snake_case__						= 0
       snake_case__						= []
       snake_case__						= 1_00_00
       while rp < n_repeat and mse >= error_accuracy:
         snake_case__						= 0
         print(F"""-------------Learning Time {rp}--------------"""   )
         for p in range(len(_a   )   ):
           # print('------------Learning Image: %d--------------'%p)
           snake_case__						= np.asmatrix(datas_train[p]   )
           snake_case__						= np.asarray(datas_teach[p]   )
           snake_case__				,				snake_case__						= self.convolute(
               _a     ,		self.conva     ,		self.w_conva     ,		self.thre_conva     ,		conv_step=self.step_conva     ,		)
           snake_case__						= self.pooling(_a     ,		self.size_poolinga   )
           snake_case__						= np.shape(_a   )
           snake_case__						= self._expand(_a   )
           snake_case__						= data_bp_input

           snake_case__						= np.dot(_a     ,		self.vji.T   ) - self.thre_bpa
           snake_case__						= self.sig(_a   )
           snake_case__						= np.dot(_a     ,		self.wkj.T   ) - self.thre_bpa
           snake_case__						= self.sig(_a   )

           # --------------Model Leaning ------------------------
           # calculate error and gradient---------------
           snake_case__						= np.multiply(
               (data_teach - bp_outa)     ,		np.multiply(_a     ,		(1 - bp_outa)   )   )
           snake_case__						= np.multiply(
               np.dot(_a     ,		self.wkj   )     ,		np.multiply(_a     ,		(1 - bp_outa)   )   )
           snake_case__						= np.dot(_a     ,		self.vji   )

           snake_case__						= pd_i_all / (self.size_poolinga * self.size_poolinga)
           snake_case__						= pd_conva_pooled.T.getA().tolist()
           snake_case__						= self._calculate_gradient_from_pool(
               _a     ,		_a     ,		shape_featuremapa[0]     ,		shape_featuremapa[1]     ,		self.size_poolinga     ,		)
           # weight and threshold learning process---------
           # convolution layer
           for k_conv in range(self.conva[1]   ):
             snake_case__						= self._expand_mat(pd_conva_all[k_conv]   )
             snake_case__						= self.rate_weight * np.dot(_a     ,		_a   )

             snake_case__						= self.w_conva[k_conv] + delta_w.reshape(
                 (self.conva[0], self.conva[0])   )

             snake_case__						= (
                 self.thre_conva[k_conv]
                 - np.sum(pd_conva_all[k_conv]   ) * self.rate_thre
             )
           # all connected layer
           snake_case__						= self.wkj + pd_k_all.T * bp_outa * self.rate_weight
           snake_case__						= self.vji + pd_j_all.T * bp_outa * self.rate_weight
           snake_case__						= self.thre_bpa - pd_k_all * self.rate_thre
           snake_case__						= self.thre_bpa - pd_j_all * self.rate_thre
           # calculate the sum error of all single image
           snake_case__						= np.sum(abs(data_teach - bp_outa   )   )
           error_count += errors
           # print('   ----Teach      ',data_teach)
           # print('   ----BP_output  ',bp_out3)
         snake_case__						= rp + 1
         snake_case__						= error_count / patterns
         all_mse.append(_a   )

       def draw_error():
         snake_case__						= [error_accuracy for i in range(int(n_repeat * 1.2   )   )]
         plt.plot(_a     ,		'''+-'''   )
         plt.plot(_a     ,		'''r--'''   )
         plt.xlabel('''Learning Times'''   )
         plt.ylabel('''All_mse'''   )
         plt.grid(_a     ,		alpha=0.5   )
         plt.show()

       print('''------------------Training Complished---------------------'''   )
       print((''' - - Training epoch: ''', rp, F"""     - - Mse: {mse:.6f}""")   )
       if draw_e:
         draw_error()
       return mse

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Optional[Any]   ):
       # model predict
       snake_case__						= []
       print('''-------------------Start Testing-------------------------'''   )
       print((''' - - Shape: Test_Data  ''', np.shape(_a   ))   )
       for p in range(len(_a   )   ):
         snake_case__						= np.asmatrix(datas_test[p]   )
         snake_case__				,				snake_case__						= self.convolute(
             _a     ,		self.conva     ,		self.w_conva     ,		self.thre_conva     ,		conv_step=self.step_conva     ,		)
         snake_case__						= self.pooling(_a     ,		self.size_poolinga   )
         snake_case__						= self._expand(_a   )

         snake_case__						= data_bp_input
         snake_case__						= bp_outa * self.vji.T - self.thre_bpa
         snake_case__						= self.sig(_a   )
         snake_case__						= bp_outa * self.wkj.T - self.thre_bpa
         snake_case__						= self.sig(_a   )
         produce_out.extend(bp_outa.getA().tolist()   )
       snake_case__						= [list(map(self.do_round     ,		_a   )   ) for each in produce_out]
       return np.asarray(_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:str   ):
       # return the data of image after convoluting process so we can check it out
       snake_case__						= np.asmatrix(_a   )
       snake_case__				,				snake_case__						= self.convolute(
           _a     ,		self.conva     ,		self.w_conva     ,		self.thre_conva     ,		conv_step=self.step_conva     ,		)
       snake_case__						= self.pooling(_a     ,		self.size_poolinga   )

       return data_conveda, data_pooleda


if __name__ == "__main__":
   pass



"""simple docstring"""



import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


a       :						List[str]       		=		logging.get_logger(__name__)




class 		a_    :







    def __init__(      self      :							List[str]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Tuple  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = question_encoder
        _UpperCAmelCase        = generator
        _UpperCAmelCase        = self.question_encoder







    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							str  )		->Dict:






        '''simple docstring'''






        if os.path.isfile(__UpperCamelCase  ):
            raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""  )
        os.makedirs(__UpperCamelCase  ,       exist_ok=__UpperCamelCase  )
        _UpperCAmelCase        = os.path.join(__UpperCamelCase  ,       """question_encoder_tokenizer"""  )
        _UpperCAmelCase        = os.path.join(__UpperCamelCase  ,       """generator_tokenizer"""  )
        self.question_encoder.save_pretrained(__UpperCamelCase  )
        self.generator.save_pretrained(__UpperCamelCase  )







    @classmethod
    def   _snake_case       (      cls      :							Optional[Any]  ,       __UpperCamelCase      :							Tuple  ,       **__UpperCamelCase      :							Optional[Any]  )		->Tuple:






        '''simple docstring'''






        from ..auto.tokenization_auto import AutoTokenizer

        _UpperCAmelCase        = kwargs.pop("""config"""  ,       __UpperCamelCase  )

        if config is None:
            _UpperCAmelCase        = RagConfig.from_pretrained(__UpperCamelCase  )

        _UpperCAmelCase        = AutoTokenizer.from_pretrained(
            __UpperCamelCase  ,       config=config.question_encoder  ,       subfolder="""question_encoder_tokenizer"""  )
        _UpperCAmelCase        = AutoTokenizer.from_pretrained(
            __UpperCamelCase  ,       config=config.generator  ,       subfolder="""generator_tokenizer"""  )
        return cls(question_encoder=__UpperCamelCase  ,       generator=__UpperCamelCase  )







    def __call__(      self      :							Any  ,       *__UpperCamelCase      :							List[Any]  ,       **__UpperCamelCase      :							Optional[Any]  )		->Optional[Any]:






        '''simple docstring'''






        return self.current_tokenizer(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Union[str, Any]  ,       *__UpperCamelCase      :							List[Any]  ,       **__UpperCamelCase      :							Tuple  )		->Any:






        '''simple docstring'''






        return self.generator.batch_decode(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Optional[int]  ,       *__UpperCamelCase      :							Optional[int]  ,       **__UpperCamelCase      :							List[str]  )		->Union[str, Any]:






        '''simple docstring'''






        return self.generator.decode(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = self.question_encoder







    def   _snake_case       (      self      :							Tuple  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = self.generator







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Optional[List[str]] = None  ,       __UpperCamelCase      :							Optional[int] = None  ,       __UpperCamelCase      :							Optional[int] = None  ,       __UpperCamelCase      :							str = "longest"  ,       __UpperCamelCase      :							str = None  ,       __UpperCamelCase      :							bool = True  ,       **__UpperCamelCase      :							Tuple  ,       )		->BatchEncoding:






        '''simple docstring'''






        warnings.warn(
            """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
            """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
            """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
            """details"""  ,       __UpperCamelCase  ,       )
        if max_length is None:
            _UpperCAmelCase        = self.current_tokenizer.model_max_length
        _UpperCAmelCase        = self(
            __UpperCamelCase  ,       add_special_tokens=__UpperCamelCase  ,       return_tensors=__UpperCamelCase  ,       max_length=__UpperCamelCase  ,       padding=__UpperCamelCase  ,       truncation=__UpperCamelCase  ,       **__UpperCamelCase  ,       )
        if tgt_texts is None:
            return model_inputs
        # Process tgt_texts
        if max_target_length is None:
            _UpperCAmelCase        = self.current_tokenizer.model_max_length
        _UpperCAmelCase        = self(
            text_target=__UpperCamelCase  ,       add_special_tokens=__UpperCamelCase  ,       return_tensors=__UpperCamelCase  ,       padding=__UpperCamelCase  ,       max_length=__UpperCamelCase  ,       truncation=__UpperCamelCase  ,       **__UpperCamelCase  ,       )
        _UpperCAmelCase        = labels["""input_ids"""]
        return model_inputs